Marketing Analytics Data Scientist

MARKETING ANALYTICS DATA SCIENTIST

$110,000 - $130,000

Fully Remote or NYC Office

An innovative marketing branding company is looking for a data scientist to join their marketing analytics team. This is a great opportunity to join a company with close-knit data driven environment that works with major companies like Shake Shack and Facebook.

THE COMPANY

You would be working for a marketing company that focuses on personalized content to maximize growth. They have an office in NYC but give flexibility to work remotely full time. They are trusted by many major enterprise level companies to provide the best omni-channel marketing experience.

THE ROLE:

As a Marketing Analytics Data Scientist, you will work cross-functionally with teams to create machine learning models to solve business problems. Your work will be end-to-end from designing machine learning models, deployment, and presentation to senior leaders utilizing visualization tools.

Models include, but are not limited to

• Customer Lifetime Value
• A/B Testing, Strategy Optimization
• Marketing Mix Modeling
• Multichannel Attribution

MUST HAVE SKILLS:

• Masters degree in a STEM field or related
• Industry experience in marketing analytics
• Strong SQL and Python/R skills
• Significant experience with data pipelining (preparing, analyzing, deploying)
• Detailed understanding of supervised & unsupervised machine learning techniques
• Basic understanding of deep learning techniques

PREFERRED:

• Experience deploying analytics in cloud technology (Azure, AWS, GCP)
• Working knowledge of visualization software (Tableau, Data Studio)
• Linear TV attribution modeling experience

BENEFITS:

• Unlimited PTO
• Bonus Opportunities
• Seamless Credits
• Comprehensive Health Care

HOW TO APPLY:

Please register your interest by sending your CV to Nitasha Khazanchi via the Apply link on this page.

KEYWORDS:

marketing analytics, machine learning, A/B testing, Python, R, SQL, visualization, tableau, data studio, AWS, Azure, customer lifetime value, marketing strategy optimization, pipelining, supervised unsupervised machine learning, deep learning, multichannel attribution, marketing mix modeling